We apply our proposed TENT model on two datasets and compare the obtained results with two other models - encoder part of the original Transformer [1]} (with the input tensor flattened) and 3d-CNN [2]}. Following the lines of [2]}, [4]}, the Mean Absolute Error (MAE) and Mean Squared Error (MSE) metrics are used to evaluate the performance of the models as follows:
\( \text{MAE}=\frac{\sum _{i=1}^{n} \vert y_i-\hat{y}_i\vert }{n},\;\;\;\;\; \text{MSE}=\frac{\sum _{i=1}^{n} (y_i-\hat{y}_i)^2}{n}.\) 
